Self-driving cars hold out the promise of being safer than manually driven cars. Yet they cannot be a 100 % safe. Collisions are sometimes unavoidable. So self-driving cars need to be programmed for how they should respond to scenarios where collisions are highly likely or unavoidable. The accident-scenarios self-driving cars might face have recently been likened to the key examples and dilemmas associated with the trolley problem. In this article, we critically examine this tempting analogy. We identify three important (...) ways in which the ethics of accident-algorithms for self-driving cars and the philosophy of the trolley problem differ from each other. These concern: the basic decision-making situation faced by those who decide how self-driving cars should be programmed to deal with accidents; moral and legal responsibility; and decision-making in the face of risks and uncertainty. In discussing these three areas of disanalogy, we isolate and identify a number of basic issues and complexities that arise within the ethics of the programming of self-driving cars. (shrink)

The prospective introduction of autonomous cars into public traffic raises the question of how such systems should behave when an accident is inevitable. Due to concerns with self-interest and liberal legitimacy that have become paramount in the emerging debate, a contractarian framework seems to provide a particularly attractive means of approaching this problem. We examine one such attempt, which derives a harm minimisation rule from the assumptions of rational self-interest and ignorance of one’s position in a future accident. (...) We contend, however, that both contractarian approaches and harm minimisation standards are flawed, due to a failure to account for the fundamental difference between those ‘involved’ and ‘uninvolved’ in an impending crash. Drawing from classical works on the trolley problem, we show how this notion can be substantiated by reference to either the distinction between negative and positive rights, or to differences in people’s claims. By supplementing harm minimisation with corresponding constraints, we can develop crash algorithms for autonomous cars which are both ethically adequate and promise to overcome certain significant practical barriers to implementation. (shrink)

The recent rapid development of information technology, such as sensing technology, communications technology, and database, allows us to use simulation experiments for analyzing serious accidents caused by hazardous chemicals. Due to the toxicity and diffusion of hazardous chemicals, these accidents often lead to not only severe consequences and economic losses, but also traffic jams at the same time. Emergency evacuation after hazardous chemical accidents is an effective means to reduce the loss of life and property and to smoothly resume the (...) transport network as soon as possible. This paper considers the dynamic changes of the hazardous chemicals’ concentration after their leakage and simulates the diffusion process. Based on the characteristics of emergency evacuation of hazardous chemical accidents, we build a mixed-integer programming model and design a heuristic algorithm using network optimization and diffusion simulation. We then verify the validity and feasibility of the algorithm using Jinan, China, as a computational example. In the end, we compare the results from different scenarios to explore the key factors affecting the effectiveness of the evacuation process. (shrink)

Traffic congestion is a common problem in many countries, especially in big cities. At present, China’s urban road traffic accidents occur frequently, the occurrence frequency is high, the accident causes traffic congestion, and accidents cause traffic congestion and vice versa. The occurrence of traffic accidents usually leads to the reduction of road traffic capacity and the formation of traffic bottlenecks, causing the traffic congestion. In this paper, the formation and propagation of traffic congestion are simulated by using the improved (...) medium traffic model, and the control strategy of congestion dissipation is studied. From the point of view of quantitative traffic congestion, the paper provides the fact that the simulation platform of urban traffic integration is constructed, and a feasible data analysis, learning, and parameter calibration method based on RBF neural network is proposed, which is used to determine the corresponding decision support system. The simulation results prove that the control strategy proposed in this paper is effective and feasible. According to the temporal and spatial evolution of the paper, we can see that the network has been improved on the whole. (shrink)

: Accidents will survive the outbreak of driverless cars, but their moral implications will suffer substantial changes. The decision made today by a human in a fraction of a second will eventually be replaced by an algorithm subject to moral scrutiny. This not only raises the question of how the algorithm should work, or whether alternatives solutions are indeed comparable, but also changes the essence of the problem: from ascertaining liability to defining desired outcomes. In this paper, I first contest (...) two possible approaches to resolving the driverless car dilemma – which I call statistical and nominal – to conclude that neither is morally sound. I then propose an alternative solution based on “time-relative equality”, or always sparing younger people. This follows from seeking an egalitarian solution, that is the least intrusive – a position I defend from a possible ageist critique. Keywords: Driverless Cars; Accident; Algorithm; Age-relative Equality Condotti verso un incidente morale Riassunto: Vi saranno incidenti anche dopo la diffusione delle auto a guida autonoma, ma le loro implicazioni morali subiranno mutamenti sostanziali. Le decisioni prese oggi in una frazione di secondo dagli esseri umani saranno alla fine sostituite da un algoritmo soggetto a controllo morale. Sorge non solo il problema di come dovrebbe funzionare questo algoritmo – o se vi siano soluzioni alternative comparabili –, ma cambia anche la sostanza del problema: dall’accertamento dell’imputabilità alla definizione dell’esito auspicato. In questo lavoro discuterò due possibili approcci per risolvere il dilemma – che chiamerò “statistico” e “nominale” –, per concluderne che non sono moralmente corretti. Proporrò quindi una soluzione alternativa, basata sulla “eguaglianza tempo-relativa”, il principio per cui bisogna sempre salvare il più giovane. Questo segue dal cercare una soluzione egalitarista, che sia la meno intrusiva – una posizione che difenderò da una possibile critica che la vede come discriminatoria verso gli anziani. Parole chiave: Automobili a guida automatica; Incidenti; Algoritmo; Eguaglianza relativa all’età. (shrink)

Almost all participants in the debate about the ethics of accidents with self-driving cars have so far assumed moral universalism. However, universalism may be philosophically more controversial than is commonly thought, and may lead to undesirable results in terms of non-moral consequences and feasibility. There thus seems to be a need to also start considering what I refer to as the “relativistic car” — a car that is programmed under the assumption that what is morally right, wrong, good, bad, etc. (...) is determined by the moral beliefs of one’s society or culture. My investigation of this idea involves six steps. First, I explain why and how the moral universalism/relativism debate is relevant to the issue of self-driving cars. Second, I argue that there are good reasons to consider accident algorithms that assume relativism. Third, I outline how a relativistic car would be programmed to behave. Fourth, I address what advantages such a car would have, both in terms of its non-moral consequences and feasibility. Fifth, I address the relativistic car’s disadvantages. Finally, I qualify and conclude my considerations. (shrink)

How should driverless vehicles respond to situations of unavoidable personal harm? This paper takes up the case of self-driving cars as a prominent example of algorithmic moral decision-making, an emergent type of morality that is evolving at a high pace in a digitised business world. As its main contribution, it juxtaposes dilemma decision situations relating to ethical crash algorithms for autonomous cars to two edge cases: the case of manually driven cars facing real-life, mundane accidents, on the one hand, (...) and the dilemmatic situation in theoretically constructed trolley cases, on the other. The paper identifies analogies and disanalogies between the three cases with regard to decision makers, decision design, and decision outcomes. The findings are discussed from the angle of three perspectives: aspects where analogies could be found, those where the case of self-driving cars has turned out to lie in between both edge cases, and those where it entirely departs from either edge case. As a main result, the paper argues that manual driving as well as trolley cases are suitable points of reference for the issue of designing ethical crash algorithms only to a limited extent. Instead, a fundamental epistemic and conceptual divergence of dilemma decision situations in the context of self-driving cars and the used edge cases is substantiated. Finally, the areas of specific need for regulation on the road to introducing autonomous cars are pointed out and related thoughts are sketched through the lens of the humanistic paradigm. (shrink)

The discourse around self-driving cars has been dominated by an emphasis on their potential to reduce the number of accidents. At the same time, proponents acknowledge that self-driving cars would inevitably be involved in fatal accidents where moral algorithms would decide the fate of those involved. This is a necessary trade-off, proponents suggest, in order to reap the benefits of this new technology. In this article, I engage this argument, demonstrating how an undue optimism and enthusiasm about this technology (...) is obscuring our ability to see what is at stake and explaining how moving beyond the dominant utilitarian framings around this technology opens up a space for both ethical inquiry and innovative design. I suggest that a genuine caring concern for the many lives lost in car accidents now and in the future—a concern that transcends false binary trade-offs and that recognizes the systemic biases and power structures that make certain groups more vulnerable than others—could serve as a starting point to rethink mobility, as it connects to the design of our cities, the well-being of our communities, and the future of our planet. (shrink)

Around 1950, B.A. Trakhtenbrot proved an important undecidability result (known, by a pure accident, as \Trakhtenbrot's theorem"): there is no algorithm to decide, given a rst-order sentence, whether the sentence is satis able in some nite model. The result is in fact true even if we restrict ourselves to languages that has only one binary relation Tra63]. It is hardly conceivable that at that time Prof. Trakhtenbrot expected his result to in uence the development of the theory of relational (...) databases query languages, but it did. This perhaps is not surprising in view of the following two facts: 1) The theory of relational databases is strongly rooted in logic and can easily be abstracted to make it a branch of mathematical logic. 2) The main interest in the theory of relational databases is in nite relations. In the rst section we explain those two points in more detail. Then, in section 2 of the present paper, we discuss the question: \What constitutes a reasonable query to a database?" The discussion naturally leads to a certain class of queries, known as the \domain independent" queries, as an ideal query language. At this point Trakhtenbrot's theorem appears as a major obstacle, since it easily implies that this ideal class is undecidable. Real query languages cannot be allowed to have this property. Section 3 outlines then several possible ways to circumvent this di culty. The following section explains the.. (shrink)

The gradual increase in the density of highway vehicles and traffic flow makes the abnormal driving state of vehicles an indispensable tool for assisting traffic dispatch. Intelligent transportation systems can detect and track vehicles in real time, acquire characteristics such as vehicle traffic, vehicle speed, vehicle flow density, and vehicle trajectory, and further perform advanced tasks such as vehicle trajectory. The detection of abnormal vehicle trajectory is an important content of vehicle trajectory understanding. And the development of the Internet of (...) Things and 5G technology has led to a continuous increase in the rate of data information circulation. The “Internet of Vehicles” generated based on the practice of 5G communication technology constitutes a vehicle abnormal trajectory detection system, which has very high feasibility and safety and stability. Therefore, this research is aimed at the needs of preventing major accidents and forensic analysis during highway vehicles. Based on the integration of the Internet of Things 5G communication technology, a trajectorial anomaly detection of highway vehicle trajectory based on the integration of the Internet of Things 5G is proposed. By accurately sensing unsafe events at the perception layer, network layer, and application layer, the vehicle driving trajectory state is divided into several simple semantic representations. The semantic representation is analyzed, and then the moving target detection and moving target tracking algorithms needed to extract the vehicle trajectory are introduced. Through video detection and tracking of moving vehicle targets, the driving trajectory of the vehicle is obtained, and the movement characteristics of the vehicle in each frame of image are extracted. According to the relationship between the trajectory of the vehicle and the lane line, the vehicle trajectory analysis is realized, and then it is judged whether the vehicle has abnormal trajectory. Compared with the traditional method of manually detecting the driving condition of the vehicle, the abnormal trajectory detection of the vehicle based on the integration of the Internet of Things and 5G can quickly detect the abnormal trajectory of the vehicle in the traffic monitoring video. (shrink)

In lieu of an abstract, here is a brief excerpt of the content:Diagnostic WannabesJennifer Radden, PhD (bio)Saunders explores challenges for the clinician faced with self-styled sufferers from attention deficit hyperactivity disorder, post-traumatic stress disorder, bipolar disorder, autism spectrum disorder (ASD), and fibromyalgia. The diagnostic system was not meant to be used as “a scaffold for identity,” she points out. Yet wannabe patients now step into the clinic wielding self-proclaimed diagnoses as social identities. Saunders explains the context where such phenomena arise, (...) and offers guidelines for clinicians addressing this new reality. To do so, she enlists Rashed’s innovative normative approach to the so-called boundary problem of assigning, and providing justification for, the contested line between normal and disordered. The boundary problem itself seems to rise to prominence during our current era of increasingly dimensional thinking, it is worth noting, with psychiatric diagnoses taken to refer to points on a continuum, rather than to discrete categorical disease entities.On the likely sources of these ‘diagnostic’ social identities, as well as reasons why certain diagnoses attach to them more commonly than others, Saunders points to social trends (including what she asserts to be a “post-stigma” cultural environment), the failure of efforts to identify neurobiological markers for psychiatric disorder, the ubiquity of social media, the algorithms themselves, and the hyperconnected existence of contemporary times. Such narratives also offer the comforts of a sense of belonging and explanations for perceived inadequacies, she rightly observes.Despite today’s virtual and other inducements, the problem introduced here is not an entirely new one. The first part of the seventeenth century in Europe saw an apparent “epidemic” of what was known as “melancholy,” a condition associated with the spleen, whose symptoms loosely resembled those of today’s depression and anxiety. Much of this suffering was undeniably real, but much was a fashionable, identity-conferring pose. “Every distemper of the body now is complicated with spleen,” the poet John Donne ironically observes in a letter dated 1622, “and when we were young men we scarce ever heard of the spleen. In our declinations now, every accident is accompanied with heavy clouds of melancholy”1 (Gosse, 2019). By then, the Melancholic Man (or homo melancholicus), with his surfeit of spleen, was an unmistakable character type—or social identity, as we would now say. Recognized and saluted in centuries-long literary and illustrative traditions, the type was an anchoring element of the humoral medicine which, despite the gradual emergence of more empirical science, had endured since Galenic times.Melancholy’s closest descendants, affective depressive and anxiety disorders, are today less evident among the troubling identities Saunders discusses—somewhat preempted, she points out, by more cognitively-based diagnoses such as ASD. [End Page 279] This is itself an ironic reminder of the vagaries of cultural attitudes, cautioning us to remember the broader context where these particular social identities are selected for emulation and adoption.The lure of the melancholy man’s2 social identity is well understood: already normalized, within the humoral system in which excess black bile afflicted roughly one in four people, it had been valorized for centuries—arguably by Aristotelian writing (in the dubious ‘Problems’), during the Renaissance by Ficino, and Shakespeare—even by Freud in his 1916 ‘Mourning and Melancholia.’ Through most of Western history, the positive attributes and benefits of the dark moods of melancholy have needed no further explaining or justifying. They glow with glamorous associations.Applying Rashed’s conditions for appropriately acknowledging and recognizing the fit of a person’s chosen social identity, Saunders‘ guidance for clinicians clarifies boundary matters. And it illustrates how to understand, regulate and communicate the appropriate norms in ways that can dissuade those whose personal growth and mental health are not served by claiming such identities.With at least some of the popular social identities Saunders describes, I think we can helpfully continue the comparison with the homo melancholicus. For the ‘neurodiversity’ rhetoric of our own times contains forces promoting both the normalization and the valorization of syndromes such as ASD. Pointing to analogies with differences of gender, ethnicity and culture, neurodiversity theorizing argues for a respect for, and appreciation of, difference. Mental or cognitive variations are equally natural and valuable, it is... (shrink)

Metal roof enclosure system is an important part of steel structure construction. In recent years, it has been widely used in large-scale public or industrial buildings such as stadiums, airport terminals, and convention centers. Affected by bad weather, various types of accidents on metal roofs frequently occurred, causing huge property losses and adverse effects. Because of wide span, long service life and hidden fault of metal roof, the manual inspection of metal roof has low efficiency, poor real-time performance, and it (...) is difficult to find hidden faults. On the basis of summarizing the working principle of metal roof and cause of accidents, this paper classifies the fault types of metal roofs in detail and establishes a metal roof monitoring and fault diagnosis system using distributed multisource heterogeneous sensors and Zigbee wireless sensor networks. Monitoring data from strain gauge, laser ranging sensor, and ultrasonic ranging sensor is utilized comprehensively. By extracting time domain feature, the data trend characteristics and correlation characteristics are analyzed and fused to eliminate erroneous data and find superficial faults such as sensor drift and network interruption. Aiming to the hidden faults including plastic deformation and bolt looseness, an SVM fault diagnosis algorithm based on RBF kernel function is designed and applied to diagnose metal roof faults. The experimental results show that the RBF-SVM algorithm can achieve high classification accuracy. (shrink)

How should driverless vehicles respond to situations of unavoidable personal harm? This paper takes up the case of self-driving cars as a prominent example of algorithmic moral decision-making, an emergent type of morality that is evolving at a high pace in a digitised business world. As its main contribution, it juxtaposes dilemma decision situations relating to ethical crash algorithms for autonomous cars to two edge cases: the case of manually driven cars facing real-life, mundane accidents, on the one hand, (...) and the dilemmatic situation in theoretically constructed trolley cases, on the other. The paper identifies analogies and disanalogies between the three cases with regard to decision makers, decision design, and decision outcomes. The findings are discussed from the angle of three perspectives: aspects where analogies could be found, those where the case of self-driving cars has turned out to lie in between both edge cases, and those where it entirely departs from either edge case. As a main result, the paper argues that manual driving as well as trolley cases are suitable points of reference for the issue of designing ethical crash algorithms only to a limited extent. Instead, a fundamental epistemic and conceptual divergence of dilemma decision situations in the context of self-driving cars and the used edge cases is substantiated. Finally, the areas of specific need for regulation on the road to introducing autonomous cars are pointed out and related thoughts are sketched through the lens of the humanistic paradigm. (shrink)

Functionalism, which views consciousness as the product of the processing of stimuli by the brain, is perhaps the dominant view among researchers in the cognitive sciences and associated fields. However, as a workable scientific model of consciousness, it has been marred by a singular lack of tangible success, except at the broadest levels of explanation. This paper argues that this is not an accident, and that in its standard construal it is simply too unwieldy to assume the burden of (...) full-fledged theory. In its place, a reduced functionalism is introduced by applying the principle of parsimony successively to the elements of standard functionalism until only a minimal framework remains. This simpler account states that consciousness is a function of instantaneous causal relations between processing elements rather the putative algorithm such relations are instantiating. It is then argued as a corollary that the only such relations that matter are those in which reciprocal influences are at play. Thus, purely afferent and efferent causal relations are pruned from consideration. The theory resulting from the addition of this corollary is shown to have good correspondence with a number of recent neurophysiologically-motivated approaches to consciousness, including those that stress the importance of reentry, those that view synchrony as a key independent variable, and those that highlight the importance of the accessibility of conscious contents to multiple processing modules. In addition, the theory is shown to be consistent with recent results in the literature on masking, and those in the literature on binocular rivalry. The paper concludes by arguing that the theoretical and empirical difficulties inherent in consciousness research imply that the principle of parsimony must occupy a more central role in consciousness research than it would in ordinary scientific discourse. (shrink)

Human contact is a key issue in social interactions for autonomous systems since robots are increasingly appearing everywhere, which has led to a higher risk of conflict. Particularly in the real world, collisions between humans and machines may result in catastrophic accidents or damaged goods. In this paper, a novel stop strategy related to autonomous systems is proposed. This control method can eliminate the vibrations produced by a system’s movement by analysing the poles and zeros in the model of autonomous (...) vehicles and goods. Using the pole placement technique, the motion of a system is guaranteed to be more stable, more flexible and smoother. Moreover, several control profiles are employed in the switching mechanism to choose the proper vibration-free effect. The main contributions of this paper are the recommendation of an active stopping planner using different smooth generators from a modelling study, the validation of their physical characteristics and the launching of a switching algorithm based on the socially aware navigation framework of a robot. This theoretical work is based on the virtual environment of MATLAB, and the experiment is implemented in the practical platform of an automated guided vehicle. From these results, it can be seen that the proposed approach is robust, effective and feasible for applications in storehouse management, public transportation or factory manufacturing. (shrink)

Algorithms wield increasing control over our lives—over which jobs we get, whether we're granted loans, what information we're exposed to online, and so on. Algorithms can, and often do, wield their power in a biased way, and much work has been devoted to algorithmic bias. In contrast, algorithmic neutrality has gone largely neglected. I investigate three questions about algorithmic neutrality: What is it? Is it possible? And when we have it in mind, what can we learn about algorithmic (...) bias? (shrink)

Algorithms can now identify patterns and correlations in the (big) datasets, and predict outcomes based on those identified patterns and correlations with the use of machine learning techniques and big data, decisions can then be made by algorithms themselves in accordance with the predicted outcomes. Yet, algorithms can inherit questionable values from the datasets and acquire biases in the course of (machine) learning, and automated algorithmic decision-making makes it more difficult for people to see algorithms as (...) biased. While researchers have taken the problem of algorithmic bias seriously, but the current discussion on algorithmic fairness tends to conceptualize ‘fairness’ in algorithmic fairness primarily as a technical issue and attempts to implement pre-existing ideas of ‘fairness’ into algorithms. In this paper, I show that such a view of algorithmic fairness as technical issue is unsatisfactory for the type of problem algorithmic fairness presents. Since decisions on fairness measure and the related techniques for algorithms essentially involve choices between competing values, ‘fairness’ in algorithmic fairness should be conceptualized first and foremost as a political issue, and it should be (re)solved by democratic communication. The aim of this paper, therefore, is to explicitly reconceptualize algorithmic fairness as a political question and suggest the current discussion of algorithmic fairness can be strengthened by adopting the accountability for reasonableness framework. (shrink)

To address the rising concern that algorithmic decision-making may reinforce discriminatory biases, researchers have proposed many notions of fairness and corresponding mathematical formalizations. Each of these notions is often presented as a one-size-fits-all, absolute condition; however, in reality, the practical and ethical trade-offs are unavoidable and more complex. We introduce a new approach that considers fairness—not as a binary, absolute mathematical condition—but rather, as a relational notion in comparison to alternative decision-making processes. Using U.S. mortgage lending as an example use (...) case, we discuss the ethical foundations of each definition of fairness and demonstrate that our proposed methodology more closely captures the ethical trade-offs of the decision-maker. (shrink)

Should decision-making algorithms be held to higher standards of transparency than human beings? The way we answer this question directly impacts what we demand from explainable algorithms, how we govern them via regulatory proposals, and how explainable algorithms may help resolve the social problems associated with decision making supported by artificial intelligence. Some argue that algorithms and humans should be held to the same standards of transparency and that a double standard of transparency is hardly justified. (...) We give two arguments to the contrary and specify two kinds of situations for which higher standards of transparency are required from algorithmic decisions as compared to humans. Our arguments have direct implications on the demands from explainable algorithms in decision-making contexts such as automated transportation. (shrink)

For most of human history, human knowledge was considered to be something that was stored and captured by words. This began to change when Galileo said that the book of nature is written in the language of mathematics. Today, Dan Dennett and many others argue that all genuine scientific knowledge is in the form of mathematical algorithms. However, recently discovered neurocomputational algorithms can be used to justify the claim that there is genuine knowledge which is non-algorithmic. The fact (...) that these algorithms use prototype deployment, rather than mathematics or logic, gives us good reason to believe that there is a kind of knowledge that we derive from stories that is different from our knowledge of algorithms. Even though we would need algorithms to build a system that can make sense out of stories, we do not need to use algorithms when we ourselves embody a system that learns from stories. The success of the Galilean perspective in the physical sciences has often resulted in an attempt to mathematize the humanities. I am arguing that the dynamic neurocomputational perspective can give us a better understanding of how we get knowledge and wisdom from the stories told by disciplines such as Literature, History, Anthropology and Theology. This new neurological data can be used to justify the traditional pedagogy of these disciplines, which originally stressed the telling of stories rather than the learning of algorithms. (shrink)

Algorithmic systems and predictive analytics play an increasingly important role in various aspects of modern life. Scholarship on the moral ramifications of such systems is in its early stages, and much of it focuses on bias and harm. This paper argues that in understanding the moral salience of algorithmic systems it is essential to understand the relation between algorithms, autonomy, and agency. We draw on several recent cases in criminal sentencing and K–12 teacher evaluation to outline four key ways (...) in which issues of agency, autonomy, and respect for persons can conflict with algorithmic decision-making. Three of these involve failures to treat individual agents with the respect they deserve. The fourth involves distancing oneself from a morally suspect action by attributing one’s decision to take that action to an algorithm, thereby laundering one’s agency. (shrink)

In information societies, operations, decisions and choices previously left to humans are increasingly delegated to algorithms, which may advise, if not decide, about how data should be interpreted and what actions should be taken as a result. More and more often, algorithms mediate social processes, business transactions, governmental decisions, and how we perceive, understand, and interact among ourselves and with the environment. Gaps between the design and operation of algorithms and our understanding of their ethical implications can (...) have severe consequences affecting individuals as well as groups and whole societies. This paper makes three contributions to clarify the ethical importance of algorithmic mediation. It provides a prescriptive map to organise the debate. It reviews the current discussion of ethical aspects of algorithms. And it assesses the available literature in order to identify areas requiring further work to develop the ethics of algorithms. (shrink)

Machine intelligence already helps medical staff with a number of tasks. Ethical decision-making, however, has not been handed over to computers. In this proof-of-concept study, we show how an algorithm based on Beauchamp and Childress’ prima-facie principles could be employed to advise on a range of moral dilemma situations that occur in medical institutions. We explain why we chose fuzzy cognitive maps to set up the advisory system and how we utilized machine learning to train it. We report on the (...) difficult task of operationalizing the principles of beneficence, non-maleficence and patient autonomy, and describe how we selected suitable input parameters that we extracted from a training dataset of clinical cases. The first performance results are promising, but an algorithmic approach to ethics also comes with several weaknesses and limitations. Should one really entrust the sensitive domain of clinical ethics to machine intelligence? (shrink)

In recent years, the ethical impact of AI has been increasingly scrutinized, with public scandals emerging over biased outcomes, lack of transparency, and the misuse of data. This has led to a growing mistrust of AI and increased calls for mandated ethical audits of algorithms. Current proposals for ethical assessment of algorithms are either too high level to be put into practice without further guidance, or they focus on very specific and technical notions of fairness or transparency that (...) do not consider multiple stakeholders or the broader social context. In this article, we present an auditing framework to guide the ethical assessment of an algorithm. The audit instrument itself is comprised of three elements: a list of possible interests of stakeholders affected by the algorithm, an assessment of metrics that describe key ethically salient features of the algorithm, and a relevancy matrix that connects the assessed metrics to stakeholder interests. The proposed audit instrument yields an ethical evaluation of an algorithm that could be used by regulators and others interested in doing due diligence, while paying careful attention to the complex societal context within which the algorithm is deployed. (shrink)

This chapter focuses on one of the common fallacies in Western philosophy called “converse accident (CA)”. The fallacy of CA occurs in much the same way as the fallacy of hasty generalization. Not unlike its other related fallacy, accident, which applies a general principle to a particular case to which it does not apply, CA instead generalizes over some cases, or even over one particular case, to make a more sweeping conclusion. This fallacious way of thinking is especially (...) noxious since it often grounds racist, sexist, or other prejudiced beliefs. It should be obvious to most readers how to detect or avoid this fallacy. Thinking of any counter‐example will usually suffice to reveal this fallacy's error. We might conclude that all other fallacies covered in this book are boring and not very exciting to read about. It's up to the reader to discover whether this is an example of the fallacious reasoning that is CA. (shrink)

It is well-established that algorithms can be instruments of injustice. It is less frequently discussed, however, how current modes of AI deployment often make the very discovery of injustice difficult, if not impossible. In this article, we focus on the effects of algorithmic profiling on epistemic agency. We show how algorithmic profiling can give rise to epistemic injustice through the depletion of epistemic resources that are needed to interpret and evaluate certain experiences. By doing so, we not only demonstrate (...) how the philosophical conceptual framework of epistemic injustice can help pinpoint potential, systematic harms from algorithmic profiling, but we also identify a novel source of hermeneutical injustice that to date has received little attention in the relevant literature: epistemic fragmentation. Epistemic fragmentation is a structural characteristic of algorithmically-mediated environments that isolate individuals, making it more difficult to develop, uptake and apply new epistemic resources. This, in turn, can impede the identification and conceptualisation of emerging harms in these environments. We trace the occurrence of hermeneutical injustice back to the fragmentation of the epistemic experiences of individuals, who are left more vulnerable by the inability to share, compare, and learn from shared experiences. (shrink)

Polyhedral functions provide a model for an important class of problems that includes both linear programming and applications in data analysis. General methods for minimizing such functions using the polyhedral geometry explicitly are developed. Such methods approach a minimum by moving from extreme point to extreme point along descending edges and are described generically as simplicial. The best-known member of this class is the simplex method of linear programming, but simplicial methods have found important applications in discrete approximation and statistics. (...) The general approach considered in this text, first published in 2001, has permitted the development of finite algorithms for the rank regression problem. The key ideas are those of developing a general format for specifying the polyhedral function and the application of this to derive multiplier conditions to characterize optimality. Also considered is the application of the general approach to the development of active set algorithms for polyhedral function constrained problems and associated Lagrangian forms. (shrink)

Algorithms influence every facet of modern life: criminal justice, education, housing, entertainment, elections, social media, news feeds, work… the list goes on. Delegating important decisions to machines, however, gives rise to deep moral concerns about responsibility, transparency, freedom, fairness, and democracy. Algorithms and Autonomy connects these concerns to the core human value of autonomy in the contexts of algorithmic teacher evaluation, risk assessment in criminal sentencing, predictive policing, background checks, news feeds, ride-sharing platforms, social media, and election interference. (...) Using these case studies, the authors provide a better understanding of machine fairness and algorithmic transparency. They explain why interventions in algorithmic systems are necessary to ensure that algorithms are not used to control citizens' participation in politics and undercut democracy. This title is also available as Open Access on Cambridge Core. (shrink)

Algorithmic communications pose several challenges to democracy. The three phenomena of filtering, hypernudging, and microtargeting can have the effect of polarizing an electorate and thus undermine the deliberative potential of a democratic society. Algorithms can spread fake news throughout the society, undermining the epistemic potential that broad participation in democracy is meant to offer. They can pose a threat to political equality in that some people may have the means to make use of algorithmic communications and the sophistication to (...) be immune from attempts at manipulation, while other people are vulnerable to manipulation by those who use these means. My concern here is with the danger that algorithmic communications can pose to political equality, which arises because most citizens must make decisions about what and who to support in democratic politics with only a sparse budget of time, money, and energy. Algorithmic communications such as hypernudging and microtargeting can be a threat to democratic participation when persons are operating in environments that do not conduce to political sophistication. This constitutes a deepening of political inequality. The political sophistication necessary to counter this vulnerability is rooted for many in economic life and it can and ought to be enhanced by changing the terms of economic life. (shrink)

This chapter focuses on one of the common fallacies in Western philosophy called ‘accident’. This fallacy often occurs when people let their attention become distracted by factors, which may be true, other than those relevant in an argument. While the fallacy of accident is an informal fallacy, people can imagine that it has something like this as a form: General principle or rule X applies across the board; particular case x is an example of X; and thus X (...) applies to x. One way to detect or to avoid this fallacy is to ask themselves what generalization is being applied. Once they have that figured out, they can ask themselves whether it really does apply in all cases across the board or whether there is a reason to make an exception in some particular case. (shrink)

I argue, firstly, that the accounts of 'accident' in Aristotle's Met. V 30 and in Top. I 5 cannot be used to elucidate each other: the Metaphysics passage tries to disentangle the uses of a Greek word, the Topics passage introduces technical terms for Aristotle's semantics. I then argue that the positive definition in Top. I 5 is to be understood in the following way: X is an accident of Y iff X belongs to Y and if there (...) is a Z such that X can belong to Z and also not belong to Z. Thus, being white is an accident of snow. I finally argue that certain shortcomings in the Topics account lead Aristotle to redefine accident in the Posterior Analytics. (shrink)

Algorithms: Design and Analysis is a textbook designed for undergraduate and postgraduate students of computer science engineering, information technology, and computer applications. The book offers adequate mix of both theoretical and mathematical treatment of the concepts. It covers the basics, design techniques, advanced topics and applications of algorithms. The book will also serve as a useful reference for researchers and practising programmers whointend to pursue a career in algorithm designing. The book is also indented for students preparing for (...) campus interviews and competitive examinations. (shrink)

The ever-increasing application of algorithms to decision-making in a range of social contexts has prompted demands for algorithmic accountability. Accountable decision-makers must provide their decision-subjects with justifications for their automated system’s outputs, but what kinds of broader principles should we expect such justifications to appeal to? Drawing from political philosophy, I present an account of algorithmic accountability in terms of the democratic ideal of ‘public reason’. I argue that situating demands for algorithmic accountability within this justificatory framework enables us (...) to better articulate their purpose and assess the adequacy of efforts toward them. (shrink)

Decision-making assisted by algorithms developed by machine learning is increasingly determining our lives. Unfortunately, full opacity about the process is the norm. Would transparency contribute to restoring accountability for such systems as is often maintained? Several objections to full transparency are examined: the loss of privacy when datasets become public, the perverse effects of disclosure of the very algorithms themselves, the potential loss of companies’ competitive edge, and the limited gains in answerability to be expected since sophisticated (...) class='Hi'>algorithms usually are inherently opaque. It is concluded that, at least presently, full transparency for oversight bodies alone is the only feasible option; extending it to the public at large is normally not advisable. Moreover, it is argued that algorithmic decisions preferably should become more understandable; to that effect, the models of machine learning to be employed should either be interpreted ex post or be interpretable by design ex ante. (shrink)

As government pressure on major technology companies builds, both firms and legislators are searching for technical solutions to difficult platform governance puzzles such as hate speech and misinformation. Automated hash-matching and predictive machine learning tools – what we define here as algorithmic moderation systems – are increasingly being deployed to conduct content moderation at scale by major platforms for user-generated content such as Facebook, YouTube and Twitter. This article provides an accessible technical primer on how algorithmic moderation works; examines some (...) of the existing automated tools used by major platforms to handle copyright infringement, terrorism and toxic speech; and identifies key political and ethical issues for these systems as the reliance on them grows. Recent events suggest that algorithmic moderation has become necessary to manage growing public expectations for increased platform responsibility, safety and security on the global stage; however, as we demonstrate, these systems remain opaque, unaccountable and poorly understood. Despite the potential promise of algorithms or ‘AI’, we show that even ‘well optimized’ moderation systems could exacerbate, rather than relieve, many existing problems with content policy as enacted by platforms for three main reasons: automated moderation threatens to further increase opacity, making a famously non-transparent set of practices even more difficult to understand or audit, further complicate outstanding issues of fairness and justice in large-scale sociotechnical systems and re-obscure the fundamentally political nature of speech decisions being executed at scale. (shrink)

While organizations today make extensive use of complex algorithms, the notion of algorithmic accountability remains an elusive ideal due to the opacity and fluidity of algorithms. In this article, we develop a framework for managing algorithmic accountability that highlights three interrelated dimensions: reputational concerns, engagement strategies, and discourse principles. The framework clarifies that accountability processes for algorithms are driven by reputational concerns about the epistemic setup, opacity, and outcomes of algorithms; that the way in which organizations (...) practically engage with emergent expectations about algorithms may be manipulative, adaptive, or moral; and that when accountability relationships are heavily burdened by the opacity and fluidity of complex algorithmic systems, the emphasis of engagement should shift to a rational communication process through which a continuous and tentative assessment of the development, workings, and consequences of algorithms can be achieved over time. The degree to which such engagement is, in fact, rational can be assessed based on four discourse-ethical principles of participation, comprehension, multivocality, and responsiveness. We conclude that the framework may help organizations and their environments to jointly work toward greater accountability for complex algorithms. It may further help organizations in reputational positioning surrounding accountability issues. The discourse-ethical principles introduced in this article are meant to elevate these positioning contests to extend beyond mere adaption or compliance and help guide organizations to find moral and forward-looking solutions to accountability issues. (shrink)

In light of the recent emergence of predictive techniques in law enforcement to forecast crimes before they occur, this paper examines the temporal operation of power exercised by predictive policing algorithms. I argue that predictive policing exercises power through a paranoid style that constitutes a form of temporal governmentality. Temporality is especially pertinent to understanding what is ethically at stake in predictive policing as it is continuous with a historical racialized practice of organizing, managing, controlling, and stealing time. After (...) first clarifying the concept of temporal governmentality, I apply this lens to Chicago Police Department’s Strategic Subject List. This predictive algorithm operates, I argue, through a paranoid logic that aims to preempt future possibilities of crime on the basis of a criminal past codified in historical crime data. (shrink)

Digital platforms and application software have changed how people work in a range of industries. Empirical studies of the gig economy have raised concerns about new systems of algorithmic management exercised over workers and how these alter the structural conditions of their work. Drawing on the republican literature, we offer a theoretical account of algorithmic domination and a framework for understanding how it can be applied to ride hail and food delivery services in the on-demand economy. We argue that certain (...) algorithms can facilitate new relationships of domination by sustaining a socio-technical system in which the owners and managers of a company dominate workers. This analysis has implications for the growing use of algorithms throughout the gig economy and broader labor market. (shrink)

Machine learning algorithms are increasingly used to shape high-stake allocations, sparking research efforts to orient algorithm design towards ideals of justice and fairness. In this research on algorithmic fairness, normative theorizing has primarily focused on identification of “ideally fair” target states. In this paper, we argue that this preoccupation with target states in abstraction from the situated dynamics of deployment is misguided. We propose a framework that takes dynamic trajectories as direct objects of moral appraisal, highlighting three respects in (...) which such trajectories can be subject to evaluation in relation to their temporal dynamics, robustness, and representation. (shrink)

Law-enforcement agencies are increasingly able to leverage crime statistics to make risk predictions for particular individuals, employing a form of inference that some condemn as violating the right to be “treated as an individual.” I suggest that the right encodes agents’ entitlement to a fair distribution of the burdens and benefits of the rule of law. Rather than precluding statistical prediction, it requires that citizens be able to anticipate which variables will be used as predictors and act intentionally to avoid (...) them. Furthermore, it condemns reliance on various indexes of distributive injustice, or unchosen properties, as evidence of law-breaking. (shrink)

Charles Perrow used the term normal accidents to characterize a type of catastrophic failure that resulted when complex, tightly coupled production systems encountered a certain kind of anomalous event. These were events in which systems failures interacted with one another in a way that could not be anticipated, and could not be easily understood and corrected. Systems of the production of expert knowledge are increasingly becoming tightly coupled. Unlike classical science, which operated with a long time horizon, many current forms (...) of expert knowledge are directed at immediate solutions to complex problems. These are prone to breakdowns like the kind discussed by Perrow. The example of the Homestake mine experiment shows that even in modern physics complex systems can produce knowledge failures that last for decades. The concept of knowledge risk is introduced, and used to characterize the risk of failure in such systems of knowledge production. (shrink)

Artificial Intelligence (AI) often outperforms human doctors in terms of decisional speed. For some diseases, the expected benefit of a fast but less accurate decision exceeds the benefit of a slow but more accurate one. In such cases, we argue, it is often justified to rely on a medical AI to maximise decision speed – even if the AI is less accurate than human doctors.

This title introduces the concepts of algorithms and sequencing in coding by using relatable real-world examples in the reader's everyday life. Vivid photographs and easy-to-read text aid comprehension for early readers. Features include a table of contents, an infographic, fun facts, Making Connections questions, a glossary, and an index. QR Codes in the book give readers access to book-specific resources to further their learning. Aligned to Common Core Standards and correlated to state standards. Cody Koala is an imprint of (...) Pop!, a division of ABDO. (shrink)

A best-seller in its French edition, the construction of this book is original and its success in the French market demonstrates its appeal. It is based on three principles: 1. An organization of the chapters by families of algorithms : exhaustive search, divide and conquer, etc. At the contrary, there is no chapter only devoted to a systematic exposure of, say, algorithms on strings. Some of these will be found in different chapters. 2. For each family of (...) class='Hi'>algorithms, an introduction is given to the mathematical principles and the issues of a rigorous design, with one or two pedagogical examples. 3. For its most part, the book details 150 problems, spanning on seven families of algorithms. For each problem, a precise and progressive statement is given. More important, a complete solution is detailed, with respect to the design principles that have been presented ; often, some classical errors are pointed at. Roughly speaking, two thirds of the book are devoted to the detailed rational construction of the solutions. (shrink)

Inspired by recent breakthroughs in predictive modeling, practitioners in both industry and government have turned to machine learning with hopes of operationalizing predictions to drive automated decisions. Unfortunately, many social desiderata concerning consequential decisions, such as justice or fairness, have no natural formulation within a purely predictive framework. In efforts to mitigate these problems, researchers have proposed a variety of metrics for quantifying deviations from various statistical parities that we might expect to observe in a fair world and offered a (...) variety of algorithms in attempts to satisfy subsets of these parities or to trade o the degree to which they are satised against utility. In this paper, we connect this approach to fair machine learning to the literature on ideal and non-ideal methodological approaches in political philosophy. The ideal approach requires positing the principles according to which a just world would operate. In the most straightforward application of ideal theory, one supports a proposed policy by arguing that it closes a discrepancy between the real and the perfectly just world. However, by failing to account for the mechanisms by which our non-ideal world arose, the responsibilities of various decision-makers, and the impacts of proposed policies, naive applications of ideal thinking can lead to misguided interventions. In this paper, we demonstrate a connection between the fair machine learning literature and the ideal approach in political philosophy, and argue that the increasingly apparent shortcomings of proposed fair machine learning algorithms reflect broader troubles faced by the ideal approach. We conclude with a critical discussion of the harms of misguided solutions, a reinterpretation of impossibility results, and directions for future research. (shrink)

Artificial intelligence (AI) systems are increasingly adopted to make decisions in domains such as business, education, health care, and criminal justice. However, such algorithmic decision systems can have prevalent biases against marginalized social groups and undermine social justice. Explainable artificial intelligence (XAI) is a recent development aiming to make an AI system’s decision processes less opaque and to expose its problematic biases. This paper argues against technical XAI, according to which the detection and interpretation of algorithmic bias can be handled (...) more or less independently by technical experts who specialize in XAI methods. Drawing on resources from feminist epistemology, we show why technical XAI is mistaken. Specifically, we demonstrate that the proper detection of algorithmic bias requires relevant interpretive resources, which can only be made available, in practice, by actively involving a diverse group of stakeholders. Finally, we suggest how feminist theories can help shape integrated XAI: an inclusive social-epistemic process that facilitates the amelioration of algorithmic bias. (shrink)

ArgumentUsing a striking example from the history of applied psychology, the concept of accident proneness, this paper suggests that historians of science may still find viable the idea of simultaneous discovery or construction of a scientific idea. Accident proneness was discovered independently in Germany and in Britain during the period of World War I. Later on, in 1926, the idea was independently formulated and named in both countries. The evidence shows not only striking simultaneity but true novelty and (...) commensurateness of the two formulations that crystallized at the same time in parallel, but distinctly separate, settings. (shrink)